A Database Implementation for the Study of Crystalline Silicon PV Modules Behavior

2020 ◽  
Author(s):  
Tevi Jean-Philippe Gabriel ◽  
Faye Marie Emilienne ◽  
Edjadessamam Akoro ◽  
Diouf Djicknoum ◽  
Sene Moustapha ◽  
...  
Keyword(s):  
Crystalline Silicon ◽  
Pv Modules ◽  
Database Implementation

scholarly journals Efficient Cell Segmentation from Electroluminescent Images of Single-Crystalline Silicon Photovoltaic Modules and Cell-Based Defect Identification Using Deep Learning with Pseudo-Colorization

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4292
Author(s):  
Horng-Horng Lin ◽  
Harshad Kumar Dandage ◽  
Keh-Moh Lin ◽  
You-Teh Lin ◽  
Yeou-Jiunn Chen
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Crystalline Silicon ◽  
Cell Segmentation ◽  
Solar Pv ◽  
Single Crystalline Silicon ◽  
Defect Inspection ◽  
Defect Identification ◽  
Pv Modules ◽  
A Cell

Solar cells may possess defects during the manufacturing process in photovoltaic (PV) industries. To precisely evaluate the effectiveness of solar PV modules, manufacturing defects are required to be identified. Conventional defect inspection in industries mainly depends on manual defect inspection by highly skilled inspectors, which may still give inconsistent, subjective identification results. In order to automatize the visual defect inspection process, an automatic cell segmentation technique and a convolutional neural network (CNN)-based defect detection system with pseudo-colorization of defects is designed in this paper. High-resolution Electroluminescence (EL) images of single-crystalline silicon (sc-Si) solar PV modules are used in our study for the detection of defects and their quality inspection. Firstly, an automatic cell segmentation methodology is developed to extract cells from an EL image. Secondly, defect detection can be actualized by CNN-based defect detector and can be visualized with pseudo-colors. We used contour tracing to accurately localize the panel region and a probabilistic Hough transform to identify gridlines and busbars on the extracted panel region for cell segmentation. A cell-based defect identification system was developed using state-of-the-art deep learning in CNNs. The detected defects are imposed with pseudo-colors for enhancing defect visualization using K-means clustering. Our automatic cell segmentation methodology can segment cells from an EL image in about 2.71 s. The average segmentation errors along the x-direction and y-direction are only 1.6 pixels and 1.4 pixels, respectively. The defect detection approach on segmented cells achieves 99.8% accuracy. Along with defect detection, the defect regions on a cell are furnished with pseudo-colors to enhance the visualization.

scholarly journals Feasibility Study of the Technical and Economic Performance of Grid-Connected PV System for Selected Rooftops in UTHM

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Mohamad Fakrie Mohamad Ali ◽  
◽  
Mohd Noor Abdullah ◽  
Keyword(s):  
Feasibility Study ◽  
Crystalline Silicon ◽  
Electricity Consumption ◽  
Payback Period ◽  
Net Energy ◽  
Pv System ◽  
Pv Module ◽  
Electricity Bill ◽  
Pv Modules ◽  
Energy Metering

This paper presents the feasibility study of the technical and economic performances of grid-connected photovoltaic (PV) system for selected rooftops in Universiti Tun Hussein Onn Malaysia (UTHM). The analysis of the electricity consumption and electricity bill data of UTHM campus show that the monthly electricity usage in UTHM campus is very high and expensive. The main purpose of this project is to reduce the annual electricity consumption and electricity bill of UTHM with Net Energy Metering (NEM) scheme. Therefore, the grid-connected PV system has been proposed at Dewan Sultan Ibrahim (DSI), Tunku Tun Aminah Library (TTAL), Fakulti Kejuruteraan Awam dan Alam Bina (FKAAS) and F2 buildings UTHM by using three types of PV modules which are mono-crystalline silicon (Mono-Si), poly-crystalline silicon (Poly-Si) and Thin-film. These three PV modules were modeled, simulated and calculated using Helioscope software with the capacity of 2,166.40kWp, 2,046.20kWp and 1,845kWp respectively for the total rooftop area of 190,302.9 ft². The economic analysis was conducted on the chosen three installed PV modules using RETScreen software. As a result, the Mono-Si showed the best PV module that can produce 2,332,327.40 kWh of PV energy, 4.4% of CO₂ reduction, 9.3 years of payback period considering 21 years of the contractual period and profit of RM4,932,274.58 for 11.7 years after payback period. Moreover, the proposed installation of 2,166.40kWp (Mono-SI PV module) can reduce the annual electricity bill and CO2 emission of 3.6% (RM421,561.93) and 4.4% (1,851.40 tCO₂) compared to the system without PV system.

Detection of shading effect by using the current and voltage at maximum power point of crystalline silicon PV modules

Solar Energy ◽  
2020 ◽  
Vol 211 ◽  
pp. 1365-1372
Author(s):  
Manit Seapan ◽  
Yoshihiro Hishikawa ◽  
Masahiro Yoshita ◽  
Keiichi Okajima
Keyword(s):  
Crystalline Silicon ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Shading Effect ◽  
Pv Modules ◽  
Power Point

scholarly journals Design for Recycling Principles Applicable to Selected Clean Energy Technologies: Crystalline-Silicon Photovoltaic Modules, Electric Vehicle Batteries, and Wind Turbine Blades

2020 ◽  
Vol 6 (4) ◽  
pp. 761-774
Author(s):  
Alex Norgren ◽  
Alberta Carpenter ◽  
Garvin Heath
Keyword(s):  
Wind Turbine ◽  
Crystalline Silicon ◽  
Raw Materials ◽  
Turbine Blades ◽  
Lithium Ion ◽  
Clean Energy ◽  
Raw Material ◽  
Wind Turbine Blades ◽  
Design For Recycling ◽  
Pv Modules

Abstract The global growth of clean energy technology deployment will be followed by parallel growth in end-of-life (EOL) products, bringing both challenges and opportunities. Cumulatively, by 2050, estimates project 78 million tonnes of raw materials embodied in the mass of EOL photovoltaic (PV) modules, 12 billion tonnes of wind turbine blades, and by 2030, 11 million tonnes of lithium-ion batteries. Owing partly to concern that the projected growth of these technologies could become constrained by raw material availability, processes for recycling them at EOL continue to be developed. However, none of these technologies are typically designed with recycling in mind, and all of them present challenges to efficient recycling. This article synthesizes and extends design for recycling (DfR) principles based on a review of published industrial and academic best practices as well as consultation with experts in the field. Specific principles developed herein apply to crystalline-silicon PV modules, batteries like those used in electric vehicles, and wind turbine blades, while a set of broader principles applies to all three of these technologies and potentially others. These principles are meant to be useful for stakeholders—such as research and development managers, analysts, and policymakers—in informing and promoting decisions that facilitate DfR and, ultimately, increase recycling rates as a way to enhance the circularity of the clean energy economy. The article also discusses some commercial implications of DfR. Graphical Abstract

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